• Title, Summary, Keyword: EM algorithm

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A Fast EM Algorithm for Gaussian Mixtures

  • Jung, Hye-Kyung;Seo, Byung-Tae
    • Communications for Statistical Applications and Methods
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    • v.19 no.1
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    • pp.157-168
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    • 2012
  • The EM algorithm is the most important tool to obtain the maximum likelihood estimator in finite mixture models due to its stability and simplicity. However, its convergence rate is often slow because the conventional EM algorithm is based on a large missing data space. Several techniques have been proposed in the literature to reduce the missing data space. In this paper, we review existing methods and propose a new EM algorithm for Gaussian mixtures, which reduces the missing data space while preserving the stability of the conventional EM algorithm. The performance of the proposed method is evaluated with other existing methods via simulation studies.

A Novel Expectation-Maximization based Channel Estimation for OFDM Systems (Expectation-Maximization 기반의 새로운 OFDM 채널 추정 방식)

  • Kim, Nam-Kyeom;Sohn, In-Soo;Shin, Jae-Ho
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.4C
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    • pp.397-402
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    • 2009
  • Accurate estimation of time-selective fading channel is a difficult problem in OFDM(Orthogonal Frequency Division Multiplexing) system. There are many channel estimation algorithms that are very weak in noisy channel. For solving this problem, we use EM (Expectation-Maximization) algorithm for iterative optimization of the data. We propose an EM-LPC algorithm to estimate the time-selective fading. The proposed algorithm improves of the BER performance compared to EM based channel estimation algorithm and reduces the iteration number of the EM loop. We simulated the uncoded system. If coded system use the EM-LPC algorithm, the performance are enhanced because of the coding gain. The EM-LPC algorithm is able to apply to another communication system, not only OFDM systems. The image processing of the medical instruments that the demand of accurate estimation can also use the proposed algorithm.

Speedup of EM Algorithm by Binning Data for Normal Mixtures (혼합정규분포의 모수 추정에서 구간도수 EM 알고리즘의 실행 속도 개선)

  • Oh, Chang-Hyuck
    • Communications for Statistical Applications and Methods
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    • v.15 no.1
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    • pp.1-11
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    • 2008
  • For a large data set the high computational cost of estimating the parameters of normal mixtures with the conventional EM algorithm is crucially impedimental in applying the algorithm to the areas requiring high speed computation such as real-time speech recognition. Simulations show that the binned EM algorithm, being compared to the standard one, significantly reduces the cost of computation without loss in accuracy of the final estimates.

A Study for Efficient EM Algorithms for Estimation of the Proportion of a Mixed Distribution (분포 혼합비율의 모수추정을 위한 효율적인 알고리즘에 관한 연구)

  • 황강진;박경탁;유희경
    • Journal of the Korean Society for Quality Management
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    • v.30 no.4
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    • pp.68-77
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    • 2002
  • EM algorithm has good convergence rate for numerical procedures which converges on very small step. In the case of proportion estimation in a mixed distribution which has very big incomplete data or of update of new data continuously, however, EM algorithm highly depends on a initial value with slow convergence ratio. There have been many studies to improve the convergence rate of EM algorithm in estimating the proportion parameter of a mixed data. Among them, dynamic EM algorithm by Hurray Jorgensen and Titterington algorithm by D. M. Titterington are proven to have better convergence rate than the standard EM algorithm, when a new data is continuously updated. In this paper we suggest dynamic EM algorithm and Titterington algorithm for the estimation of a mixed Poisson distribution and compare them in terms of convergence rate by using a simulation method.

Accelerating the EM Algorithm through Selective Sampling for Naive Bayes Text Classifier (나이브베이즈 문서분류시스템을 위한 선택적샘플링 기반 EM 가속 알고리즘)

  • Chang Jae-Young;Kim Han-Joon
    • The KIPS Transactions:PartD
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    • v.13D no.3
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    • pp.369-376
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    • 2006
  • This paper presents a new method of significantly improving conventional Bayesian statistical text classifier by incorporating accelerated EM(Expectation Maximization) algorithm. EM algorithm experiences a slow convergence and performance degrade in its iterative process, especially when real online-textual documents do not follow EM's assumptions. In this study, we propose a new accelerated EM algorithm with uncertainty-based selective sampling, which is simple yet has a fast convergence speed and allow to estimate a more accurate classification model on Naive Bayesian text classifier. Experiments using the popular Reuters-21578 document collection showed that the proposed algorithm effectively improves classification accuracy.

Incremental EM algorithm with multiresolution kd-trees and cluster validation and its application to image segmentation (다중해상도 kd-트리와 클러스터 유효성을 이용한 점증적 EM 알고리즘과 이의 영상 분할에의 적용)

  • Lee, Kyoung-Mi
    • Journal of Korean Institute of Intelligent Systems
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    • v.25 no.6
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    • pp.523-528
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    • 2015
  • In this paper, we propose a new multiresolutional and dynamic approach of the EM algorithm. EM is a very popular and powerful clustering algorithm. EM, however, has problems that indexes multiresolution data and requires a priori information on a proper number of clusters in many applications, To solve such problems, the proposed EM algorithm can impose a multiresolution kd-tree structure in the E-step and allocates a cluster based on sequential data. To validate clusters, we use a merge criteria for cluster merging. We demonstrate the proposed EM algorithm outperforms for texture image segmentation.

Improved Kalman filter performance via EM algorithm (EM 알고리즘을 통한 칼만 필터의 성능 개선)

  • Kang, Jee-Hye;Kim, Sung-Soo
    • Proceedings of the KIEE Conference
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    • pp.2615-2617
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    • 2003
  • The Kalman filter is a recursive Linear Estimator for the linear dynamic systems(LDS) affected by two different noises called process noise and measurement noise both of which are uncorrelated white. The Expectation Maximization(EM) algorithm is employed in this paper as a preprocessor to reinforce the effectiveness of Kalman estimator. Particularly, we focus on the relation between Kalman filter and EM algorithm in the LDS. In this paper, we propose a new algorithm to improve the performance on the parameter estimation via EM algorithm, which improves the overall process of Kalman filtering. Since Kalman filter algorithm not only needs the system parameters but also is very sensitive the initial state conditions, the initial conditions decided through EM turns out to be very effective. In experiments, the computer simulation results ate provided to demonstrate the superiority of the proposed algorithm.

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New EM algorithm for Principal Component Analysis (주성분 분석을 위한 새로운 EM 알고리듬)

  • 안종훈;오종훈
    • Proceedings of the Korean Information Science Society Conference
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    • pp.529-531
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    • 2001
  • We present an expectation-maximization algorithm for principal component analysis via orthogonalization. The algorithm finds actual principal components, whereas previously proposed EM algorithms can only find principal subspace. New algorithm is simple and more efficient thant probabilistic PCA specially in noiseless cases. Conventional PCA needs computation of inverse of the covariance matrices, which makes the algorithm prohibitively expensive when the dimensions of data space is large. This EM algorithm is very powerful for high dimensional data when only a few principal components are needed.

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Pattern Analysis and Performance Comparison of Lottery Winning Numbers

  • Jung, Yong Gyu;Han, Soo Ji;kim, Jae Hee
    • International Journal of Internet, Broadcasting and Communication
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    • v.6 no.1
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    • pp.16-22
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    • 2014
  • Clustering methods such as k-means and EM are the group of classification and pattern recognition, which are used in management science and literature search widely. In this paper, k-means and EM algorithm are compared the performance using by Weka. The winning Lottery numbers of 567 cases are experimented for our study and presentation. Processing speed of the k-means algorithm is superior to the EM algorithm, which is about 0.08 seconds faster than the other. As the result it is summerized that EM algorithm is better than K-means algorithm with comparison of accuracy, precision and recall. While K-means is known to be sensitive to the distribution of data, EM algorithm is probability sensitive for clustering.

An Explicit Solution of EM Algorithm in Image Deblurring: Image Restoration without EM iterations (영상흐림보정에서 EM 알고리즘의 일반해: 반복과정을 사용하지 않는 영상복원)

  • Kim, Seung-Gu
    • Communications for Statistical Applications and Methods
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    • v.16 no.3
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    • pp.409-419
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    • 2009
  • In this article, an explicit solution of the EM algorithm for the image deburring is presented. To obtain the restore image from the strictly iterative EM algorithm is quite time-consumed and impractical in particular when the underlying observed image is not small and the number of iterations required to converge is large. The explicit solution provides a quite reasonable restore image although it exploits the approximation in the outside of the valid area of image, and also allows to obtain the effective EM solutions without iteration process in real-time in practice by using the discrete finite Fourier transformation.